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The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.

This chapter presents and discusses various steps to ensure empirical reliability and theoretical validity in the construction of competence scales in graduate surveys. The development of a scale to assess demands of the teacher profession and related abilities in graduates for a German tracer study project serves as an example. Confirmatory factor analysis (CFA), principal component analysis (PCA) and Cronbach’s coefficient alpha are employed to test the reliability of the scale. Differing results illustrate how the method applied influences decisions in the process of developing a scale. Our findings show that multidimensionality can only be tested appropriately by CFA; PCA renders no feasible or similar results to CFA depending on the predetermination of the number of factors; Cronbach’s alpha produces misleading results as the prerequisite assumption of unidimensionality is violated by the data.

Mediating effects are often tested using hierarchical multiple regression (HMR) procedures. Typical of the HMR-based strategies is the very frequently cited and widely used procedure described by Baron and Kenny (1986). Unfortunately, there are several important problems with it. More specifically, as we demonstrate below, it: (a) is of virtually no value for buttressing claims of mediating effects for data from non-experimental research; (b) produces erroneous inferences about the existence of mediating effects for misspecified mediation models; and (c) is incapable of providing credible evidence of such effects in a large proportion of cases, even for properly specified mediation models. We detail a number of important implications of our analyses.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.